Seminars

Directed protein evolution is vital for biomedicine but faces challenges like experimental complexity and inefficient optimization. While protein language models can guide fitness landscapes, they struggle with diverse protein families. We present EVOLVEpro, a few-shot active learning framework combining PLMs and regression models to enhance protein activity. It achieves up to 100-fold property improvements across six proteins in RNA, genome editing, and antibody applications, opening new possibilities for AI-guided protein engineering.